Partially Observable Monte Carlo Planning (POMCP) [1]} is an online Monte-Carlo based solver for POMDPs. It uses Monte-Carlo Tree Search (MCTS) for selecting, at each time-step, an action which approximates the optimal one.
The Monte Carlo tree is generated by performing a certain number of simulations (\(nSim\) ) from the current belief. A big advantage of POMCP is that it enables to scale to large state spaces because it never represents the complete policy but it generates only the part of the policy related to the belief states actually seen during the plan execution. Moreover, the local policy approximation is generated online using a simulator of the environment, namely a function that given the current state and an action provides the new state and an observation according to the POMDP transition and observation models.
